A couple of weeks ago, the Atlas research group started to apply their geolocation data and methods to analyze the effectiveness of the social distancing policies adopted in the New York metropolitan area in response to the coronavirus pandemic. There’s no way to empirically measure the impact of these social distancing measures in real time on the spread of Covid-19, the respiratory disease caused by the virus. You can only measure their impact retrospectively, or simulate what might happen in the future based on past data.

However, you can empirically address a key set of important questions including how social distancing policies changed mobility and social behavior and the extent to which people are following these measures.

The initial findings reveal that the area’s social distancing policies have led to major changes in where people spend their time and how they interact with each other: “The number of social contacts in places decreased by 93% from 75 to 5,” says the report, where social contact is defined as being within 25 meters (82 feet) of each other for at least 5 minutes. Also, "social distancing policies have greatly reduced relative differences between different demographic groups as nearly everyone’s mobility and social contacts has been dramatically reduced.”

The change became significant only after non-essential business closure measures were put in place. Retail food and essential supply stores are now the most common places for social contacts. After those measures were introduced, about 5.5% of New Yorkers started to spend time in places beyond the metro area, including New Jersey (37%), upstate NY (23%), Pennsylvania (9.8%) and Florida (6.7%).

More detailed findings can be found in the draft report. Let me briefly describe the sources of the data used in the analysis as well as the methods used to preserve data privacy.

The kind of central government directives that were deployed in China to combat its Covid-19 outbreak aren’t applicable in the US and other free-market democracies. In these countries, it’s important to turn to sophisticated data analysis methods that are compliant with privacy policies.

The primary data source for the Atlas of Inequality project is anonymized location data from a variety of applications on smartphone devices. The data comes from Cuebiq, a geolocation-based intelligence and measurement company, and in particular from Cuebiq’s Data for Good initiative which makes its data available for academic research and humanitarian programs.

For the NY social distancing analysis, the company collects anonymized records of timestamped GPS points from users who opted-in to share their data anonymously. Mobility data is only extracted from those users who opted in to share their data through a GDPR and CCPA compliant framework. Residential and work areas data is then aggregated to the Census Block Group level, allowing for the demographic analysis while obfuscating the exact location where the anonymous users live and work.

“The data we received is constructed from the sequence of pings reported by devices,” explains the report. “This results in a dataset of the public places where many people have stayed (with high spatial accuracy) corresponding to the points of interest that people typically visit and the most likely census tracts of where these device owners live and work.” A stay is defined as a place where an anonymous user stopped for at least 5 minutes.

The analysis is limited to data from people who were active during the period February 17 to March 9 and for whom there is location data reporting that they stayed in their home Census Block Group more than 10 days. The dataset includes information on 567,000 people.

There’s much, much more to be done. The next empirical question, according to the report's conclusion, is how effective these social distance policies are at reducing the spread of the coronavirus.

Irving Wladawsky-Berger worked at IBM from 1970 to 2007, and has been a strategic adviser to Citigroup, HBO and Mastercard and a visiting professor at Imperial College. He's been affiliated with MIT since 2005, and is a regular contributor to CIO Journal.